SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

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Showing 876–900 of 6689 papers

TitleStatusHype
From Image to Imuge: Immunized Image GenerationCode1
Finetuning CLIP to Reason about Pairwise DifferencesCode1
Causal Inference via Style Transfer for Out-of-distribution GeneralisationCode1
Fully Spiking Denoising Diffusion Implicit ModelsCode1
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative ModelsCode1
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic ModelsCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiesCode1
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space OptimizationCode1
Few-shot Image Generation via Cross-domain CorrespondenceCode1
GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face SynthesisCode1
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-LocalizationCode1
Few-shot Semantic Image Synthesis Using StyleGAN PriorCode1
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
Few-Shot Defect Image Generation via Defect-Aware Feature ManipulationCode1
GANs in computer vision ebookCode1
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input MechanismsCode1
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumCode1
Few-Shot Human Motion Transfer by Personalized Geometry and Texture ModelingCode1
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of ExpertsCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
FlexIT: Towards Flexible Semantic Image TranslationCode1
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data SynthesisCode1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision TasksCode1
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified